Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an appliance level by analyzing the total aggregated data measurements monitored from a single point. Most prominent existing solutions use deep learning techniques resulting in models with millions of parameters and a high computational burden. Some of these solutions use the turn-on transient response of the target appliance to calculate its energy consumption, while others require the total operation cycle. In the latter case, disaggregation is performed either with delay (in the order of minutes) or only for past events. In this paper, a real-time NILM system is proposed. The scope of the proposed NILM algorithm is to detect the turning-on of a target appliance by processing the measured active power transient response and estimate its consumption in real-time. The proposed system consists of three main blocks, i.e., an event detection algorithm, a convolutional neural network classifier and a power estimation algorithm. Experimental results reveal that the proposed system can achieve promising results in real-time, presenting high computational and memory efficiency.
Given the additional awareness of the increasing energy demand and gas emissions’ effects, the decarbonization of the transportation sector is of great significance. In particular, the adoption of electric vehicles (EVs) seems a promising option, under the condition that public charging infrastructure is available. However, devising a pricing and scheduling strategy for public EV charging stations is a non-trivial albeit important task. The reason is that a sub-optimal decision could lead to high waiting times or extreme changes to the power load profile. In addition, in the context of the problem of optimal pricing and scheduling for EV charging stations, the interests of different stakeholders ought to be taken into account (such as those of the station owner and the EV owners). This work proposes a deep reinforcement learning-based (DRL) agent that can optimize pricing and charging control in a public EV charging station under a real-time varying electricity price. The primary goal is to maximize the station’s profits while simultaneously ensuring that the customers’ charging demands are also satisfied. Moreover, the DRL approach is data-driven; it can operate under uncertainties without requiring explicit models of the environment. Variants of scheduling and DRL training algorithms from the literature are also proposed to ensure that both the conflicting objectives are achieved. Experimental results validate the effectiveness of the proposed approach.
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